CN104537438A - Forecast and monitoring method for peak-hour power usage - Google Patents
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Abstract
The invention discloses a forecast and monitoring method for peak-hour power usage. The method comprises the steps that classification for power usage data is conducted according to preset temperature threshold, and the classification of the power usage data in a period of time pending to be forecasted is determined; collecting the power usage data of specified classification of all the electric equipment pending to be tested is conducted in a period of time pending to be forecasted; conditional random field model is established, and the collected power usage data which is taken as a training sample is used for estimating the conditional probability distribution function of the power usage; anticipated power usage of the next forecast period of time and whether the high-demand in electricity occurs are forecasted according to the the conditional probability distribution function of the power usage and current power usage; when the peak-hour power usage is forecasted, alarm amount of information is sent to users. Accordingly, good monitoring of the power usage of the power grid can be conducted, so that the network load can be released through regulating the power of appliance in time before the peak-hour power usage occurs.
Description
Technical field
The present invention relates to network load control technique field, refer to a kind of prediction and method for supervising of peak of power consumption especially.
Background technology
Along with improving constantly of living standard, the ratio that residential electricity consumption accounts for electrical network total load constantly increases.Above concentrate because residential electricity consumption has the time, therefore will cause network load big ups and downs at short notice, and occur peak of power consumption.The measure of conventional electric power system reply peak load mainly relies on to increase capacity of installed generator and promote grid equipment power transmission and distribution abilities and realizes, thus make Generation Side and grid side equipment efficiency of usage low, serious waste resource.
As can be seen here, network load prediction regulates significant for network load.At present, the Load Forecasting of the main flow used in prior art has: forecasting by regression analysis (comprising linear regression and non-linear regression class methods), time series forecasting, grey method, neural network prediction method etc.
But it is huge that Load Forecasting of the prior art (such as, for regression class method) generally all has calculated amount, and the shortcomings such as real-time is not good enough, are therefore difficult to predict preferably the network load of resident.
Summary of the invention
In view of this, the object of the invention is to the prediction and the method for supervising that propose a kind of peak of power consumption, thus can monitor preferably the network load of resident, so that regulate appliance power to alleviate network load before peak of power consumption occurs in time.
The invention provides a kind of method for supervising of peak of power consumption based on above-mentioned purpose, the method comprises:
Temperature threshold according to presetting is classified to power consumption data, and determines the classification of the power consumption data in the time period to be predicted;
Other power consumption data of specified class of all consumers to be measured are gathered within the time period to be predicted;
Set up the condition random field models, and gathered power consumption data are estimated as training sample the conditional probability distribution function obtaining power consumption;
According to conditional probability distribution function and the current power amount of described power consumption, predict the expection power consumption of next predicted time section and whether occur peak of power consumption;
When predicting when there is peak of power consumption, send warning message to user.
Preferably, described as training sample, gathered power consumption data are estimated that the conditional probability distribution function obtaining power consumption comprises:
Initialize installation is carried out to conditional random field models;
Gathered power consumption data are carried out iterative computation in the conditional random field models after training sample input initialization is arranged, and use the estimation of maximum likelihood parameter estimation algorithm to obtain the value of described feature weight parameter lambda, thus obtain the conditional probability distribution function of power consumption.
Preferably, cloud computing technology is used to calculate the conditional probability distribution function of power consumption.
Preferably, the conditional probability distribution function of described power consumption is:
Wherein, the conditional probability distribution function that p (y|x, λ) is power consumption, x is current power amount, and y is expection power consumption, and λ is feature weight parameter, and Z (x) is normalized factor, and f is proper vector.
Preferably, describedly Initialize installation carried out to conditional random field models comprise:
The initial value of feature weight parameter lambda is set to 0.
Preferably, whether next predicted time section of described prediction occurs that peak of power consumption comprises:
Pre-set power consumption threshold value P
awith the probability threshold value P on power consumption peak
t;
The value calculating expection power consumption according to the conditional probability distribution function of described power consumption is greater than P
aprobability;
When the value of expection power consumption is greater than P
aprobability be more than or equal to the probability threshold value P on power consumption peak
ttime, judge will occur peak of power consumption in next predicted time section.
As can be seen from above, in method for supervising due to peak of power consumption in the present invention, employ domestic consumer's consumer founding mathematical models that condition random field theoretical log amount is huge, and the trend of resident's total electricity consumption is predicted with this, by predicting the generation of peak of power consumption, peak of power consumption is monitored, thus reasonable adjusting electricity of can taking measures in advance uses, peak is cut down to reach, the object of balancing electric power relation between supply and demand, thus can utilize the automatic control technology of Smart Home before the peak of power consumption of expection occurs, regulate appliance power to reach the object alleviating network load in time, therefore than other predictions of the prior art and regulate and control method, there is higher accuracy and real-time, and combined with intelligent household technology, network load can be regulated fast and effectively when not affecting existing power grid operation.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the method for supervising of peak of power consumption in the embodiment of the present invention;
Embodiment
For making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in more detail.
Present embodiments provide a kind of method for supervising of peak of power consumption.
Fig. 1 is the schematic flow sheet of the method for supervising of peak of power consumption in the embodiment of the present invention.As shown in Figure 1, the method for supervising of the peak of power consumption in the embodiment of the present invention mainly comprises:
Step 11, the temperature threshold according to presetting is classified to power consumption data, and determines the classification of the power consumption data in the time period to be predicted.
In the inventive solutions, need to consider weather conditions, such as temperature is on the impact of power consumption, and the power consumption data under each class particular weather condition have corresponding rule.Therefore, a temperature threshold can be pre-set, then according to the temperature threshold preset, power consumption data be classified.Such as, in the preferred embodiment, the power consumption data under cold snap, nice and cool weather, warm weather and hot weather four kinds of conditions can be divided into.
After power consumption data are classified, the classification of the power consumption data within the time period to be predicted can be determined according to the Temperature numerical in the time period to be predicted.
Step 12, gathers other power consumption data of specified class of all consumers to be measured within the time period to be predicted.
Preferably, in a particular embodiment of the present invention, can by intelligent domestic system and other power consumption data of the specified class of all consumers to be measured within the time period to be predicted in bond networking technology acquisition system, and by the power consumption data summarization that collects in the database of server so that in subsequent step 13 using gathered power consumption data as training sample.
Step 13, set up the condition random field models, and the conditional probability distribution function gathered power consumption data being obtained power consumption as training sample.
It condition random field (Conditional Random Fields, CRF) theoretical essence is a kind of method of statistical learning.Statistical learning, by analyzing mass data to build probability statistics model, extracts the feature of data and makes prediction to the trend of data.
Conditional random field models is that a kind of being used for marks and the statistical model of cutting serialized data.These data are predetermined to be has markov attribute.This model, under the condition of the observation sequence of given needs mark, calculates the joint probability of whole flag sequence.The distribution occasion attribute of flag sequence, can allow the good matching real data of condition random field, and in these data, the conditional probability of flag sequence depends on dependent in observation sequence, interactional feature, and carried out the significance level of representation feature with different weights by imparting feature.
Conditional random field models is the model set up for one group of stochastic variable with Markov property.Markov property means that to open up the stochastic variable of mending associating based on non-directed graph only relevant with adjacent variable, and independent with non-conterminous variable.
In the inventive solutions, using the variable of the power consumption of each tested consumer as conditional random field models, thus can set up the condition random field models, and according to set up set up the condition random field models, condition random field theory and method of estimation prediction electricity consumption trend.
Preferably, in a particular embodiment of the present invention, the conditional probability distribution function of described power consumption can be expressed as:
Wherein, the conditional probability distribution function that p (y|x, λ) is power consumption, x is current power amount, and y is expection power consumption, and λ is feature weight parameter, and Z (x) is normalized factor, and f is proper vector.
According to above formula, when preliminary set up the condition random field models, the feature weight parameter lambda in the conditional probability distribution function of the power consumption in above-mentioned conditional random field models is unknown parameter (i.e. the parameter of value the unknown).Therefore, in the inventive solutions, gathered power consumption data can be estimated as training sample the value obtaining above-mentioned feature weight parameter lambda, thus obtain the conditional probability distribution function of power consumption.
Such as, preferably, in the preferred embodiment, described as training sample, gathered power consumption data are estimated that the conditional probability distribution function obtaining power consumption can be realized by step as described below:
Step 131, carries out Initialize installation to conditional random field models.
Such as, preferably, in a particular embodiment of the present invention, describedly Initialize installation is carried out to conditional random field models comprise: the initial value of feature weight parameter lambda is set to 0.
Certainly, in the inventive solutions, also can need according to practical application the value initial value of feature weight parameter lambda being set to other.
Step 132, gathered power consumption data are carried out iterative computation in the conditional random field models after training sample input initialization is arranged, and use the estimation of maximum likelihood parameter estimation algorithm to obtain the value of described feature weight parameter lambda, thus obtain the conditional probability distribution function of power consumption.
In the inventive solutions, because gathered power consumption data are larger, calculation of complex, general computing machine has been difficult to meet above-mentioned computation requirement, therefore, preferably, in a particular embodiment of the present invention, cloud computing technology can be used to calculate the conditional probability distribution function of power consumption.
Step 14, according to conditional probability distribution function and the current power amount of described power consumption, predicts the expection power consumption of next predicted time section and whether occurs peak of power consumption.
Owing to obtaining the conditional probability distribution function of power consumption in step 13, therefore in this step can according to the conditional probability distribution function of this power consumption and current power amount, the expection power consumption of next predicted time section is predicted, and can predict in next predicted time section whether there will be peak of power consumption.
Such as, preferably, in a particular embodiment of the present invention, whether next predicted time section of described prediction occurs that peak of power consumption comprises:
Step 141, pre-sets power consumption threshold value P
awith the probability threshold value P on power consumption peak
t.
Step 142, the value calculating expection power consumption according to the conditional probability distribution function of described power consumption is greater than P
aprobability.
Step 143, when the value of expection power consumption is greater than P
aprobability be more than or equal to the probability threshold value P on power consumption peak
t(i.e. p (y|x>P
a)>=P
t) time, judge will occur peak of power consumption in next predicted time section.
Step 15, when predicting when there is peak of power consumption, sends warning message to user, thus user can be intervened according to the electricity consumption behavior of default electricity consumption strategy to user, reduces power consumption, to avoid occurring peak of power consumption.
Such as, in the preferred embodiment, the described electricity consumption behavior to user is carried out intervention and can be comprised: the large power consumption electrical equipment (such as, water heater etc.) controlling running reduces operate power, even closes.
By above-mentioned step 11 ~ 15, the monitoring to peak of power consumption can be realized.
In summary, in method for supervising due to peak of power consumption in the present invention, employ domestic consumer's consumer founding mathematical models that condition random field theoretical log amount is huge, and the trend of resident's total electricity consumption is predicted with this, by predicting the time period of peak of power consumption, to take measures in advance reasonable adjusting power consumption, peak is cut down to reach, the object of balancing electric power relation between supply and demand, thus can utilize the automatic control technology of Smart Home before the peak of power consumption of expection occurs, regulate appliance power to reach the object alleviating network load in time, therefore than other predictions of the prior art and regulate and control method, there is higher accuracy and real-time, and combined with intelligent household technology, network load can be regulated fast and effectively when not affecting existing power grid operation.
Those of ordinary skill in the field are to be understood that: the foregoing is only specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any amendment made, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (6)
1. a method for supervising for peak of power consumption, is characterized in that, the method comprises
Temperature threshold according to presetting is classified to power consumption data, and determines the classification of the power consumption data in the time period to be predicted;
Other power consumption data of specified class of all consumers to be measured are gathered within the time period to be predicted;
Set up the condition random field models, and gathered power consumption data are estimated as training sample the conditional probability distribution function obtaining power consumption;
According to conditional probability distribution function and the current power amount of described power consumption, predict the expection power consumption of next predicted time section and whether occur peak of power consumption;
When predicting when there is peak of power consumption, send warning message to user.
2. method according to claim 1, is characterized in that, described as training sample, gathered power consumption data is estimated that the conditional probability distribution function obtaining power consumption comprises:
Initialize installation is carried out to conditional random field models;
Gathered power consumption data are carried out iterative computation in the conditional random field models after training sample input initialization is arranged, and use the estimation of maximum likelihood parameter estimation algorithm to obtain the value of described feature weight parameter lambda, thus obtain the conditional probability distribution function of power consumption.
3. method according to claim 2, is characterized in that:
Cloud computing technology is used to calculate the conditional probability distribution function of power consumption.
4. method according to claim 2, is characterized in that, the conditional probability distribution function of described power consumption is:
Wherein, the conditional probability distribution function that p (y|x, λ) is power consumption, x is current power amount, and y is expection power consumption, and λ is feature weight parameter, and Z (x) is normalized factor, and f is proper vector.
5. method according to claim 4, is characterized in that, describedly carries out Initialize installation to conditional random field models and comprises:
The initial value of feature weight parameter lambda is set to 0.
6. method according to claim 1, is characterized in that, whether next predicted time section of described prediction occurs that peak of power consumption comprises:
Pre-set power consumption threshold value P
awith the probability threshold value P on power consumption peak
t;
The value calculating expection power consumption according to the conditional probability distribution function of described power consumption is greater than P
aprobability;
When the value of expection power consumption is greater than P
aprobability be more than or equal to the probability threshold value P on power consumption peak
ttime, judge will occur peak of power consumption in next predicted time section.
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Cited By (12)
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| CN105404934A (en) * | 2015-11-11 | 2016-03-16 | 北京航空航天大学 | Urban population mobile data model analysis method based on conditional random field |
| CN106196267A (en) * | 2016-07-22 | 2016-12-07 | 国网北京市电力公司 | The alarm method of electric heating equipment, device and system |
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| CN108376300A (en) * | 2018-03-02 | 2018-08-07 | 江苏电力信息技术有限公司 | A kind of user power utilization behavior prediction method based on probability graph model |
| CN109214637A (en) * | 2017-07-07 | 2019-01-15 | 中国移动通信集团陕西有限公司 | A kind of network element power consumption determines method, apparatus, storage medium and calculates equipment |
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| CN104820874A (en) * | 2015-05-18 | 2015-08-05 | 国家电网公司 | Monitoring method for power dispatching center |
| CN105404934A (en) * | 2015-11-11 | 2016-03-16 | 北京航空航天大学 | Urban population mobile data model analysis method based on conditional random field |
| CN106196267A (en) * | 2016-07-22 | 2016-12-07 | 国网北京市电力公司 | The alarm method of electric heating equipment, device and system |
| CN106196267B (en) * | 2016-07-22 | 2019-09-10 | 国网北京市电力公司 | The alarm method of electric heating equipment, device and system |
| CN106451438B (en) * | 2016-11-16 | 2018-12-21 | 国网江苏省电力公司经济技术研究院 | A kind of load setting prediction technique considering intelligent power behavior |
| CN106451438A (en) * | 2016-11-16 | 2017-02-22 | 国网江苏省电力公司经济技术研究院 | Load section predicting method considering about intelligent power use behavior |
| CN106771429A (en) * | 2016-12-28 | 2017-05-31 | 湖南大学 | Power consumption mode learning method and device and intelligent ammeter |
| CN106771429B (en) * | 2016-12-28 | 2019-07-26 | 湖南大学 | Power consumption mode learning method and device and intelligent ammeter |
| CN107490985A (en) * | 2017-05-18 | 2017-12-19 | 宝沃汽车（中国）有限公司 | Body workshop energy management method and system |
| CN109214637A (en) * | 2017-07-07 | 2019-01-15 | 中国移动通信集团陕西有限公司 | A kind of network element power consumption determines method, apparatus, storage medium and calculates equipment |
| CN109214637B (en) * | 2017-07-07 | 2020-12-08 | 中国移动通信集团陕西有限公司 | Network element power consumption determination method and device, storage medium and computing equipment |
| CN107294093A (en) * | 2017-07-26 | 2017-10-24 | 广东电网有限责任公司电力科学研究院 | Electric power data analysis method, device and intelligent terminal based on K line charts |
| CN108376300A (en) * | 2018-03-02 | 2018-08-07 | 江苏电力信息技术有限公司 | A kind of user power utilization behavior prediction method based on probability graph model |
| CN109636038A (en) * | 2018-12-12 | 2019-04-16 | 国网黑龙江省电力有限公司双鸭山供电公司 | Peak of power consumption monitoring method based on block chain |
| CN110783925A (en) * | 2019-12-02 | 2020-02-11 | 安阳师范学院 | Two-stage deployment power grid regulation and control model center control system and method |
| CN111652449A (en) * | 2020-07-17 | 2020-09-11 | 上海积成能源科技有限公司 | System model for predicting short-term photovoltaic power generation system generated energy based on conditional random field model |
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